In this manuscript a novel approach for SAR urban change detection is presented. Its peculiarity is its ability to detect the changes not directly from the measured amplitude data, but exploiting the whole complex image. In particular, the scene in modelled as a Local Gaussian Markov Random Field, and is described via the so called hyperparameters, which refers to the spatial correlation of pixels. By comparing such hyperparameters obtained from a pre-event and a post-event dataset, we can detect occurred changes. Results on real datasets show good detection accuracy together with very low false alarm rate.

SAR change detection in a Markovian Bayesian framework

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2013

Abstract

In this manuscript a novel approach for SAR urban change detection is presented. Its peculiarity is its ability to detect the changes not directly from the measured amplitude data, but exploiting the whole complex image. In particular, the scene in modelled as a Local Gaussian Markov Random Field, and is described via the so called hyperparameters, which refers to the spatial correlation of pixels. By comparing such hyperparameters obtained from a pre-event and a post-event dataset, we can detect occurred changes. Results on real datasets show good detection accuracy together with very low false alarm rate.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/31566
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